Results 41 to 50 of about 1,412,360 (277)
An Introduction to Sequential Monte Carlo
, 2020 Particle filters are about 25 years old. Initially confined to the so-called “filtering problem” (the sequential analysis of state-space models), they are now routinely applied to a large variety of sequential and non-sequential tasks and have evolved to the broader Sequential Monte Carlo (SMC) framework.
Chopin, Nicolas +1 more
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Efficient Monte Carlo filtering for discretely observed jumping processes [PDF]
, 2007 This paper addresses a tracking problem in which the unobserved process is characterised by a collection of random jump times and associated random parameters.
Simon Godsill +5 more
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INVESTIGATION OF BIOLOGICAL OBJECTS IN OPTICAL COHERENCE TOMOGRAPHY WITH DATA PROCESSING BY SEQUENTIAL MONTE CARLO METHOD [PDF]
Научно-технический вестник информационных технологий, механики и оптики, 2014 A possibility of sequential Monte Carlo method application for data processing in the full-field optical coherent tomography for studying of biological objects is demonstrated.
M. A. Volynsky +2 more
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Monte Carlo filtering of piecewise deterministic processes [PDF]
, 2011 We present efficient Monte Carlo algorithms for performing Bayesian inference in a broad class of models: those in which the distributions of interest may be represented by time marginals of continuous-time jump processes conditional on a realisation of
Godsill, S +5 more
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Sequential Monte Carlo with model tempering
Studies in Nonlinear Dynamics & Econometrics, 2023 Abstract Modern macroeconometrics often relies on time series models for which it is time-consuming to evaluate the likelihood function. We demonstrate how Bayesian computations for such models can be drastically accelerated by reweighting and mutating posterior draws from an approximating model that allows for fast likelihood ...
Marko Mlikota, Frank Schorfheide
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Multilevel sequential Monte Carlo samplers [PDF]
Stochastic Processes and their Applications, 2017 In this article we consider the approximation of expectations w.r.t. probability distributions associated to the solution of partial differential equations (PDEs); this scenario appears routinely in Bayesian inverse problems. In practice, one often has to solve the associated PDE numerically, using, for instance finite element methods and leading to a ...
Beskos, Alexandros +4 more
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Simulated redistricting plans for the analysis and evaluation of redistricting in the United States
Scientific Data, 2022 Measurement(s) redistricting, partisanship Technology Type(s) sequential Monte Carlo algorithm Factor Type(s) population deviation • compactness • county splits • racial composition • municipality splits Sample Characteristic - Organism Congressional ...
Cory McCartan +7 more
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Sequential Monte Carlo Methods in the nimble and nimbleSMC R Packages
Journal of Statistical Software, 2021 nimble is an R package for constructing algorithms and conducting inference on hierarchical models. The nimble package provides a unique combination of flexible model specification and the ability to program model-generic algorithms.
Nicholas Michaud +4 more
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Bayesian model selection for multilevel models using integrated likelihoods.
PLoS ONE, 2023 Multilevel linear models allow flexible statistical modelling of complex data with different levels of stratification. Identifying the most appropriate model from the large set of possible candidates is a challenging problem. In the Bayesian setting, the
Tom Edinburgh, Ari Ercole, Stephen Eglen
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Variational Sequential Monte Carlo
, 2017 Many recent advances in large scale probabilistic inference rely on variational methods. The success of variational approaches depends on (i) formulating a flexible parametric family of distributions, and (ii) optimizing the parameters to find the member of this family that most closely approximates the exact posterior.
Andersson Naesseth, Christian +3 more
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